Title :
Margin and domain integrated classification
Author :
Chen, Yen-Lun ; Zheng, Yuan F.
Author_Institution :
Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA
Abstract :
Multi-category classification is an ongoing research topic with numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It handles multi-class problems as a combination of several target classes plus outliers. The basic idea behind the proposed approach is that target classes possess structured characteristics while outliers scatter around in the feature space. In our approach the domain description and large-margin discrimination are adjustable and therefore higher classification accuracy leads to better performance. The properties of MDIC are analyzed and the performance comparisons using synthetic and real data are presented.
Keywords :
pattern classification; support vector machines; domain description; large margin discrimination; margin-domain integrated classification; multicategory classification; outliers; Application software; Face recognition; Handwriting recognition; Machine learning; Object recognition; Pattern classification; Performance analysis; Scattering; Support vector machine classification; Support vector machines; Pattern classification; multi-category classification; support vector domain description (SVDD); support vector machine (SVM);
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5414267